---
title: "context2vec: Learning Generic Context — Melamud 2016"
description: "Many NLP tasks need a good representation of the sentence surrounding a target word, yet unsupervised approaches have usually just averaged the word…"
authors: ["Oren Melamud", "Jacob Goldberger", "Ido Dagan"]
year: 2016
venue: "Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/K16-1006/"
doi: "10.18653/v1/k16-1006"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/"
---
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# context2vec: Learning Generic Context Embedding with Bidirectional LSTM

**Authors:** [Oren Melamud](https://md.research.elamur.ai/free/en/authors/oren-melamud/), Jacob Goldberger, [Ido Dagan](https://md.research.elamur.ai/free/en/authors/ido-dagan/) · **Year:** 2016 · **Venue:** Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/K16-1006/) · [PDF](https://aclanthology.org/K16-1006.pdf) · [DOI 10.18653/v1/k16-1006](https://doi.org/10.18653/v1/k16-1006) · [OpenAlex](https://openalex.org/W2507974895)

**Citations:** 513 citations (openalex-snapshot · 2026-10-14) · [152](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Many NLP tasks need a good representation of the sentence surrounding a target word, yet unsupervised approaches have usually just averaged the word embeddings in a fixed window (AWE), which never optimizes the context as a whole. The authors propose context2vec, an unsupervised model and toolkit that learns a generic embedding function for full, variable-length sentential contexts. Its architecture follows word2vec CBOW, but the averaged window is replaced by two independent LSTMs, one reading left-to-right and one right-to-left, that skip the target word itself. Their outputs are concatenated and passed through an MLP. Context and target embeddings share one space and are trained with word2vec's negative sampling objective on corpora of billions of words. The authors show that this setup implicitly factorizes a PMI matrix between targets and whole contexts. Relying solely on cosine similarity between embeddings, context2vec clearly outperforms AWE and performs on par with or better than the strongest previous systems for completing sentences, substituting words in context, and supervised WSD. The released pre-trained models could therefore serve as a drop-in context representation for unsupervised and semi-supervised systems.

## Key points
- **Architecture:** a bidirectional LSTM with separate left and right word embeddings, followed by an MLP with ReLU. Training uses negative sampling, and the smoothing factor α controls bias toward rare words, with higher values favoring them.
- **Benchmarks:** MSCC (sentence completion), LST-07 and LST-14 (lexical substitution), and Senseval-3 (supervised WSD).
- **Sentence completion:** on MSCC, the model trained with more iterations reaches about 64.0, against roughly 48 for AWE and 58.9 for the prior best system.
- **WSD and lexical substitution:** on Senseval-3 it scores about 72.8, near the best prior result of 74.1. On the lexical substitution benchmarks it is comparable to Melamud et al. (2015a/b), whose systems rely on exemplars or dependency parsing.
- **Resources:** source code and pre-trained models are publicly released by Bar-Ilan University.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/)

## Cited by

Cited by

152 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 140 not yet.

- [Adversarial Removal of Demographic Attributes from Text Data](https://md.research.elamur.ai/free/en/papers/elazar-2018-adversarial-removal-demographic-attributes-text/)
- [Out-of-domain FrameNet Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/hartmann-2017-out-domain-framenet-semantic-role/)
- [How Can We Know What Language Models Know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
- [Weight Poisoning Attacks on Pretrained Models](https://md.research.elamur.ai/free/en/papers/kurita-2020-weight-poisoning-attacks-pretrained-models/)
- [Large Scale Substitution-based Word Sense Induction](https://md.research.elamur.ai/free/en/papers/lex-2022-eyal-substitution-wsi/)
- [Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison](https://md.research.elamur.ai/free/en/papers/onto-2017-raganato-word-sense-disambiguation-unified-evaluation/)
- [Semi-supervised sequence tagging with bidirectional language models](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/)
- [Pre-trained Models for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
- [MultiCoPIE: A Multilingual Corpus of Potentially Idiomatic Expressions for Cross-lingual PIE Disambiguation](https://md.research.elamur.ai/free/en/papers/sentsova-2025-multicopie-multilingual-corpus-potentially-idiomatic/)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://md.research.elamur.ai/free/en/papers/yin-2020-tabert-pretraining-joint-understanding-textual/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{melamud2016context2vec,
  title = {{context2vec: Learning Generic Context Embedding with Bidirectional LSTM}},
  author = {Melamud, Oren and Goldberger, Jacob and Dagan, Ido},
  booktitle = {Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning},
  year = {2016},
  doi = {10.18653/v1/k16-1006},
  url = {https://doi.org/10.18653/v1/k16-1006}
}
```

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